From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring (with one important exception).
John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically.
In the show John reveals:
• What is more appropriate than trading individual stocks
• The one thing he relies upon in his approach to the market
• The detail surrounding his unique trading style
• What important thing underpins the market and gives every trader an edge
About INVERSE FISHER TRANSFORM:
The purpose of technical indicators is to help with your timing decisions to buy or
sell. Hopefully, the signals are clear and unequivocal. However, more often than
not your decision to pull the trigger is accompanied by crossing your fingers.
Even if you have placed only a few trades you know the drill.
In this article I will show you a way to make your oscillator-type indicators make
clear black-or-white indication of the time to buy or sell. I will do this by using the
Inverse to alter the Probability Distribution Function ( PDF ) of
your indicators. In the past12 I have noted that the PDF of price and indicators do
not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the
familiar bell-shaped curve where the long “tails” mean that wide deviations from
the mean occur with relatively low probability. The can be
applied to almost any normalized data set to make the resulting PDF nearly
Gaussian, with the result that the turning points are sharply peaked and easy to
identify. The is defined by the equation
Whereas the is expansive, the Inverse is
compressive. The Inverse is found by solving equation 1 for x
in terms of y. The Inverse is:
The transfer response of the Inverse is shown in Figure 1. If
the input falls between –0.5 and +0.5, the output is nearly the same as the input.
For larger absolute values (say, larger than 2), the output is compressed to be no
larger than unity . The result of using the Inverse is that the
output has a very high probability of being either +1 or –1. This bipolar
probability distribution makes the Inverse ideal for generating
an indicator that provides clear buy and sell signals.
// author: KIVANC @fr3762 on twitter
// creator John EHLERS
study("Inverse Fisher Transform on MFI", shorttitle="IFTMFI")
mfilength=input(5, "MFI Length")
wmalength=input(9, title="Smoothing length")
source = hlc3
up = sum(volume * (change(source) <= 0 ? 0 : source), mfilength)
lo = sum(volume * (change(source) >= 0 ? 0 : source), mfilength)
mfi = rsi(up, lo)
plot(INV, color=purple, linewidth=2)